Electrogastrography in patients with gastric motility disorders

• Published in: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Vol. 238; no. 1; p. 22
• Main Authors: Al Kafee, Abdullah, Kayar, Yusuf
• Format: Journal Article
• Language: English
• Published: England 01.01.2024
• Subjects: dyspeptic symptoms; Electrogastrography; artificial neural network; power spectral density; discrete wavelet transform; gastrointestinal electrophysiology
• ISSN: 2041-3033
• DOI: 10.1177/09544119231212269

Electrogastrography (EGG) is a novel diagnostic modality for assessing the gastrointestinal tract (GI) that generates spontaneous electrical activity and monitors gastric motility. The aim of this study was to compare patients with functional dyspepsia (FD) and diabetic gastroparesis (D-GP) with healthy controls (CT) to use established findings on abnormalities of gastric motility based on EGG characteristics. In this study, 50 patients with FD, 50 D-GP patients, and 50 CT subjects were studied to compare EGG with discrete wavelet transform models (DWT) to extract signal characteristics using a variety of different qualitative and quantitative metrics from pre-prandial and postprandial states. As a result, higher statistically significant (  < 0.05*) were found in the DWT models based on power spectral density (PSD) analysis in both states. We also present that the correlations between EGG metrics and the presence of FD, D-GP, and CT symptoms were inconsistent. This paper represents that EGG assessments of FD and D-GP patients differ from healthy controls in terms of abnormalities of gastric motility. Additionally, we demonstrate that diverse datasets showed adequate gastric motility responses to a meal. We anticipate that our method will provide a comprehensive understanding of gastric motility disorders for better treatment and monitoring in both clinical and research settings. In conclusion, we present potential future opportunities for precise gastrointestinal electrophysiological disorders.